[ https://issues.apache.org/jira/browse/HADOOP-15407?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Thomas Marquardt updated HADOOP-15407:
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Attachment: HADOOP-15407-HADOOP-15407.007.patch
> Support Windows Azure Storage - Blob file system in Hadoop
> ----------------------------------------------------------
>
> Key: HADOOP-15407
> URL: https://issues.apache.org/jira/browse/HADOOP-15407
> Project: Hadoop Common
> Issue Type: New Feature
> Components: fs/azure
> Affects Versions: 3.2.0
> Reporter: Esfandiar Manii
> Assignee: Esfandiar Manii
> Priority: Major
> Attachments: HADOOP-15407-001.patch, HADOOP-15407-002.patch, HADOOP-15407-003.patch,
HADOOP-15407-004.patch, HADOOP-15407-HADOOP-15407.006.patch, HADOOP-15407-HADOOP-15407.007.patch
>
>
> *{color:#212121}Description{color}*
> This JIRA adds a new file system implementation, ABFS, for running Big Data and Analytics
workloads against Azure Storage. This is a complete rewrite of the previous WASB driver with
a heavy focus on optimizing both performance and cost.
> {color:#212121} {color}
> *{color:#212121}High level design{color}*
> At a high level, the code here extends the FileSystem class to provide an implementation
for accessing blobs in Azure Storage. The scheme abfs is used for accessing it over HTTP,
and abfss for accessing over HTTPS. The following URI scheme is used to address individual
paths:
> {color:#212121} {color}
> {color:#212121}abfs[s]://<filesystem>@<account>.dfs.core.windows.net/<path>{color}
> {color:#212121} {color}
> {color:#212121}ABFS is intended as a replacement to WASB. WASB is not deprecated but
is in pure maintenance mode and customers should upgrade to ABFS once it hits General Availability
later in CY18.{color}
> {color:#212121}Benefits of ABFS include:{color}
> {color:#212121}· Higher scale (capacity, throughput, and IOPS) Big
Data and Analytics workloads by allowing higher limits on storage accounts{color}
> {color:#212121}· Removing any ramp up time with Storage backend partitioning;
blocks are now automatically sharded across partitions in the Storage backend{color}
> {color:#212121} . This avoids the need for using temporary/intermediate
files, increasing the cost (and framework complexity around committing jobs/tasks){color}
> {color:#212121}· Enabling much higher read and write throughput on
single files (tens of Gbps by default){color}
> {color:#212121}· Still retaining all of the Azure Blob features customers
are familiar with and expect, and gaining the benefits of future Blob features as well{color}
> {color:#212121}ABFS incorporates Hadoop Filesystem metrics to monitor the file system
throughput and operations. Ambari metrics are not currently implemented for ABFS, but will
be available soon.{color}
> {color:#212121} {color}
> *{color:#212121}Credits and history{color}*
> Credit for this work goes to (hope I don't forget anyone): Shane Mainali, {color:#212121}Thomas
Marquardt, Zichen Sun, Georgi Chalakov, Esfandiar Manii, Amit Singh, Dana Kaban, Da Zhou,
Junhua Gu, Saher Ahwal, Saurabh Pant, and James Baker. {color}
> {color:#212121} {color}
> *Test*
> ABFS has gone through many test procedures including Hadoop file system contract tests,
unit testing, functional testing, and manual testing. All the Junit tests provided with the
driver are capable of running in both sequential/parallel fashion in order to reduce the testing
time.
> {color:#212121}Besides unit tests, we have used ABFS as the default file system in Azure
HDInsight. Azure HDInsight will very soon offer ABFS as a storage option. (HDFS is also used
but not as default file system.) Various different customer and test workloads have been run
against clusters with such configurations for quite some time. Benchmarks such as Tera*, TPC-DS,
Spark Streaming and Spark SQL, and others have been run to do scenario, performance, and functional
testing. Third parties and customers have also done various testing of ABFS.{color}
> {color:#212121}The current version reflects to the version of the code tested and used
in our production environment.{color}
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